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 <p>The <b>VLFeat</b> <a
  href="http://www.gnu.org/licenses/old-licenses/gpl-2.0.html">open
  source</a> library implements popular computer vision algorithms
  including
  <em>SIFT</em>, <em>MSER</em>, <em>k-means</em>, <em>hierarchical
  k-means</em>, <em>agglomerative information bottleneck, and quick
  shift</em>.  It is written in C for efficiency and compatibility,
  with interfaces in MATLAB for ease of use, and detailed
  documentation throughout. It supports
  <em>Windows</em>, <em>Mac OS X</em>, and <em>Linux</em>. The latest version of
  VLFeat is <include src="version.html" />.</p>

 <div class="box" style="height:6em;">
  <!-- <img src="%pathto:root;images/down.png" alt="Download" style="float:left;"/> -->
  <h1>Download</h1>
  <ul>
   <li><b><a href="%pathto:root;download/vlfeat-%env:VERSION;-bin.tar.gz"
   onClick="javascript:
   pageTracker._trackPageview('/download/vlfeat-%env:VERSION;-bin.tar.gz');">VLFeat
   %env:VERSION;</a></b> (Windows, Mac, Linux)</li>
   <li><a href="%pathto:download;">Source code and installation</a></li>
   <li><a href="http://github.com/vlfeat/vlfeat/tree/master">Git
   repository</a></li>
  </ul>
 </div>

 <div class="box" style="height:6em;">
  <!-- <img src="%pathto:root;images/help.png" alt="Help" style="float:left;"/> -->
  <h1>Documentation</h1>
  <ul>
   <li><a href="%pathto:mdoc;">MATLAB commands</a></li>
   <li><a href="%pathto:api;">C API</a> with algorithm descriptions</li>
   <li><a href="%pathto:man;">Command line tools</a></li>
  </ul>

 </div><div class="box" style="height:12em;">
  <h1>Tutorials</h1>
  <ul>
  <li>Features:  <a href="%pathto:tut.sift;">SIFT</a>, <a href="%pathto:tut.mser;">MSER</a>,
    <a href="%pathto:tut.qs;">Quick shift</a></li>
  <li>Clustering: <a href="%pathto:tut.ikm;">IKM</a>, <a href="%pathto:tut.hikm;">HIKM</a>,
    <a href="%pathto:tut.aib;">AIB</a></li>
  <li>Matching:<a href="%pathto:tut.kdtree;">randomized kd-trees</a></li>
  <li><a href="%pathto:tut;">All tutorials</a></li>
  </ul>
  <h1>Example applications</h1>
  <ul>
  <li><a href="%pathto:apps.caltech-101;">Dense SIFT and spatial
  histograms for image classification.</a></li>
  </ul>
 </div>

 <div class="box" style="height:12em;">
  <h1>Citing</h1>
<pre style="font-size: .8em; color: black; margin-top: 0.5em ; margin-bottom: 1.1em; line-height:1.1em;">
@misc{vedaldi08vlfeat,
 Author = {A. Vedaldi and B. Fulkerson},
 Title = {{VLFeat}: An Open and Portable Library 
          of Computer Vision Algorithms},
 Year  = {2008},
 Howpublished = {\url{http://www.vlfeat.org/}}
</pre>
<h1>Acknowledgments</h1>
<p><a href="http://vision.ucla.edu">UCLA Vision
Lab</a>, <a href="http://www.robots.ox.ac.uk/~vgg/">Oxford VGG</a>.</p>
 </div>

 <h2 style="clear:left;">News</h2>

 <div class="clear">&nsbp;</div>
 <dl id="changes">
   <dt>10/05/2010 - VLFeat 0.9.8 released</dt>
   <dd>VLFeat 0.9.8 adds new tutorials, (hierarchical) k-means support for
     floating point data, homogeneous kernel maps, a basic implementation
     of PEGASOS for SVM learning, and many other improvements.
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.8">Details</a>].
   </dd>

   <dt>16/01/2010 - VLFeat 0.9.7 released</dt>
   <dd>VLFeat 0.9.7 updates the binary distribution to be backward
     compatible with Mac OS X 10.5 (Leopard).
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.7">Details</a>].
   </dd>

   <dt>10/01/2010 - VLFeat 0.9.6 released</dt>
   <dd>VLFeat 0.9.6 contains minor improvements to the binary
   distribution. Specifically, it makes VLFeat GNU/Linux distribution
   compatible with the older GLIBC version 2.3.
   [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.6">Details</a>].
   </dd>

   <dt>30/11/2009 - VLFeat 0.9.5 released</dt>
   <dd>VLFeat 0.9.5 adds a fast kd-tree implementation and
   SSE-acelerated vector/histogram comparison. The dense SIFT (dsift)
   implementation has also been improved. Binaries and compilation
   support for Mac OS 10.6 (Snow Leopard) and MATLAB R2009b (32 and 64
   bit) have been added
   [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.5">Details</a>].
   <blockquote>
     <b>MATLAB 7.0</b> and earlier require recompling the MEX files by
     the provided <code>vl_compile</code> command.
   </blockquote>
   </dd>
 </dl>

 <h2 style="clear:left;">Acknowledgments</h2>

 <p>Part of this work was supported by
 the <a href="http://vision.ucla.edu">UCLA Vision Lab</a> and
 the <a href="http://www.robots.ox.ac.uk/~vgg/">Oxford VGG
 Lab</a>. The authors would like to thank the many colleagues that
 have contributed to VLFeat by testing and providing helpful
 suggestions and comments.</p>

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